Quantifying the topography of rivers and their associated bedforms has been a fundamental concern of fluvial geomorphology for decades. Such data, acquired at high temporal and spatial resolutions, are increasingly in demand for process‐oriented investigations of flow hydraulics, sediment dynamics and in‐stream habitat. In these riverine environments, the most challenging region for topographic measurement is the wetted, submerged channel. Generally, dry bed topography and submerged bathymetry are measured using different methods and technology. This adds to the costs, logistical challenges and data processing requirements of comprehensive river surveys. However, some technologies are capable of measuring the submerged topography. Through‐water photogrammetry and bathymetric LiDAR are capable of reasonably accurate measurements of channel beds in clear water. While the cost of bathymetric LiDAR remains high and its resolution relatively coarse, the recent developments in photogrammetry using Structure from Motion (SfM) algorithms promise a fundamental shift in the accessibility of topographic data for a wide range of settings. Here we present results demonstrating the potential of so called SfM‐photogrammetry for quantifying both exposed and submerged fluvial topography at the mesohabitat scale. We show that imagery acquired from a rotary‐winged Unmanned Aerial System (UAS) can be processed in order to produce digital elevation models (DEMs) with hyperspatial resolutions (c. 0.02 m) for two different river systems over channel lengths of 50–100 m. Errors in submerged areas range from 0.016 m to 0.089 m, which can be reduced to between 0.008 m and 0.053 m with the application of a simple refraction correction. This work therefore demonstrates the potential of UAS platforms and SfM‐photogrammetry as a single technique for surveying fluvial topography at the mesoscale (defined as lengths of channel from c.10 m to a few hundred metres). Copyright © 2014 John Wiley & Sons, Ltd.

Recently, we have gained the opportunity to obtain very high‐resolution imagery and topographic data of rivers using drones and novel digital photogrammetric processing techniques. The high‐resolution outputs from this method are unprecedented, and provide the opportunity to move beyond river habitat classification systems, and work directly with spatially explicit continuums of data. Traditionally, classification systems have formed the backbone of physical river habitat monitoring for their ease of use, rapidity, cost efficiency, and direct comparability. Yet such classifications fail to characterize the detailed heterogeneity of habitat, especially those features which are small or marginal. Drones and digital photogrammetry now provide an alternative approach for monitoring river habitat and hydromorphology, which we review here using two case studies. First, we demonstrate the classification of river habitat using drone imagery acquired in 2012 of a 120 m section of the San Pedro River in Chile, which was at the technological limits of what could be achieved at that time. Second, we review how continuums of data can be acquired, using drone imagery acquired in 2016 from the River Teme in Herefordshire, England. We investigate the precision and accuracy of these data continuums, highlight key current challenges, and review current best practices of data collection, processing, and management. We encourage further quantitative testing and field applications. If current difficulties can be overcome, these continuums of geomorphic and hydraulic information hold great potential for providing new opportunities for understanding river systems to the benefit of both river science and management. WIREs Water 2017, 4:e1222. doi: 10.1002/wat2.1222This article is categorized under: Water and Life > Nature of Freshwater Ecosystems Water and Life > Methods

Accurate and reliable methods for quantifying grain size are important for river science, management and in various other sedimentological settings. Remote sensing offers methods of quantifying grain size, typically providing; (a) coarse outputs (c. 1 m) at the catchment scale where individual grains are at subpixel level, or; (b) fine resolution outputs (c. 1 mm) at the patch scale. Recently, approaches using unmanned aerial vehicles (UAVs) have started to fill the gap between these scales, providing hyperspatial resolution data (< 10 cm) over reaches a few hundred metres in length, where individual grains are at suprapixel level. This ‘mesoscale’ is critical to habitat assessments. Most existing UAV‐based approaches use two‐dimensional (2D) textural variables to predict grain size. Validation of results is largely absent however, despite significant differences in platform stability and image quality obtained by manned aircraft versus UAVs. Here, we provide the first quantitative assessment of the accuracy and precision of grain size estimates produced from a 2D image texture approach. Furthermore, we present a new method which predicts subaerial gravel size using three‐dimensional (3D) topographic data derived from UAV imagery. Data is collected from a small gravel‐bed river in Cumbria, UK. Results indicate that our new topographic method gives more accurate measures of grain size (mean residual error ‐0.0001 m). Better results for the image texture method may be precluded by our choice of texture measure, the scale of analysis or the effects of image blur resulting from an inadequate camera gimbal. We suggest that at our scale of assessment, grain size is more strongly related to 3D variation in elevation than to the 2D textural patterns expressed within the imagery. With on‐going improvements, our novel method has potential as the first grain size quantification approach where a trade‐off between coverage and resolution is not necessary or inherent. Copyright © 2017 John Wiley & Sons, Ltd.

Current glacier ablation models have difficulty simulating the high‐melt transition zone between clean and debris‐covered ice. In this zone, thin debris cover is thought to increase ablation compared to clean ice, but often this cover is patchy rather than continuous. There is a need to understand ablation and debris dynamics in this transition zone to improve the accuracy of ablation models and the predictions of future debris cover extent. To quantify the ablation of partially debris‐covered ice (or ‘dirty ice’), a high‐resolution, spatially continuous ablation map was created from repeat unmanned aerial systems surveys, corrected for glacier flow in a novel way using on‐glacier ablation stakes. Surprisingly, ablation is similar (range ~5 mm w.e. per day) across a wide range of percentage debris covers (~30–80%) due to the opposing effects of a positive correlation between percentage debris cover and clast size, countered by a negative correlation with albedo. Once debris cover becomes continuous, ablation is significantly reduced (by 61.6% compared to a partial debris cover), and there is some evidence that the cleanest ice (<~15% debris cover) has a lower ablation than dirty ice (by 3.7%). High‐resolution feature tracking of clast movement revealed a strong modal clast velocity where debris was continuous, indicating that debris moves by creep down moraine slopes, in turn promoting debris cover growth at the slope toe. However, not all slope margins gain debris due to the removal of clasts by supraglacial streams. Clast velocities in the dirty ice area were twice as fast as clasts within the continuously debris‐covered area, as clasts moved by sliding off their boulder tables. These new quantitative insights into the interplay between debris cover characteristics and ablation can be used to improve the treatment of dirty ice in ablation models, in turn improving estimates of glacial meltwater production. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

Much of the geomorphic work of rivers occurs underwater. As a result, high resolution quantification of geomorphic change in these submerged areas is important. Currently, to quantify this change, multiple methods are required to get high resolution data for both the exposed and submerged areas. Remote sensing methods are often limited to the exposed areas due to the challenges imposed by the water, and those remote sensing methods for below the water surface require the collection of extensive calibration data in-channel, which is time-consuming, labour-intensive, and sometimes prohibitive in dicult-to-access areas. Within this paper, we pioneer a novel approach for quantifying above- and below-water geomorphic change using Structure-from-Motion photogrammetry and investigate the implications of water surface elevations, refraction correction measures, and the spatial variability of topographic errors. We use two epochs of imagery from a site on the River Teme, Herefordshire, UK, collected using a remotely piloted aircraft system (RPAS) and processed using Structure-from-Motion (SfM) photogrammetry. For the first time, we show that: (1) Quantification of submerged geomorphic change to levels of accuracy commensurate with exposed areas is possible without the need for calibration data or a dierent method from exposed areas; (2) there is minimal dierence in results produced by dierent refraction correction procedures using predominantly nadir imagery (small angle vs. multi-view), allowing users a choice of software packages/processing complexity; (3) improvements to our estimations of water surface elevations are critical for accurate topographic estimation in submerged areas and can reduce mean elevation error by up to 73%; and (4) we can use machine learning, in the form of multiple linear regressions, and a Gaussian Naïve Bayes classifier, based on the relationship between error and 11 independent variables, to generate a high resolution, spatially continuous model of geomorphic change in submerged areas, constrained by spatially variable error estimates. Our multiple regression model is capable of explaining up to 54% of magnitude and direction of topographic error, with accuracies of less than 0.04 m. With on-going testing and improvements, this machine learning approach has potential for routine application in spatially variable error estimation within the RPAS–SfM workflow.